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3D Printed Microneedles for the Transdermal Delivery of NAD<sup>+</sup> Precursor: Toward Personalization of Skin Delivery

2024· article· en· W4402745760 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueACS Biomaterials Science & Engineering · 2024
Typearticle
Languageen
FieldPharmacology, Toxicology and Pharmaceutics
TopicAdvancements in Transdermal Drug Delivery
Canadian institutionsFraser Institute
Fundersnot available
KeywordsTransdermalMaterials sciencePersonalization3d printedNanotechnologyBiomedical engineeringComputer scienceMedicinePharmacologyWorld Wide Web

Abstract

fetched live from OpenAlex

3D printing of microneedles (μNDs) for transdermal therapy has the potential to enable patient personalization based on the target disease, site of application, and dosage requirements. To convert this concept to reality, it is necessary that the 3D printing technology can deliver high resolution, an affordable cost, and large print volumes. With the introduction of benchtop 4K and 8K 3D printers, it is now possible to manufacture medical devices like μNDs at sufficient resolution and low cost. In this research, we systematically optimized the 3D printing design parameters such as resin viscosity, print angle, layer height, and curing time to generate customizable μNDs. We have also developed an innovative 3D coating microtank device to optimize the coating method. We have applied this to the development of novel μNDs to deliver an established NAD + precursor molecule, nicotinamide mononucleotide (NMN). A methacrylate-based polymer photoresin (eSun resin) was diluted with methanol to adjust the resin viscosity. The 3D print layer height of 25 μm yielded a smooth surface, thus reducing edge-ridge mismatches. Printing μNDs at 90° to the print platform yielded 84.28 ± 2.158% ( n = 5) of the input height thus increasing the tip sharpness (48.52 ± 10.43 μm, n = 5). The formulation containing fluorescein (model molecule), sucrose (viscosity modifier), and Tween-20 (surface tension modifier) was coated on the μNDs using the custom designed microtank setup, and the amount deposited was determined fluorescently. The dye-coated μND arrays inserted into human skin ( in vitro ) showed a fluorescence signal at a depth of 150 μm ( n = 3) into the skin. After optimization of the 3D printing parameters and coating protocol using fluorescein, NMN was coated onto the μNDs, and its diffusion was assessed in full-thickness human skin in vitro using a Franz diffusion setup. Approximately 189 ± 34.5 μg (5× dipped coated μNDs) of NMN permeated through the skin and 41.2 ± 7.53 μg was left in the skin after 24 h. Multiphoton microscopy imaging of NMN-coated μND treated mouse ear skin ex vivo demonstrated significantly ( p < 0.05) increased free-unbound NADPH and reduced fluorescence lifetime of NADPH, both of which are indicative of cellular metabolic rates. Our study demonstrates that low-cost benchtop 3D printers can be used to print high-fidelity μNDs with the ability to rapidly coat and release NMN which consequently caused changes in intracellular NAD + levels.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.065
Threshold uncertainty score0.869

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.057
GPT teacher head0.363
Teacher spread0.306 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it